--- title: "ELI5: Long Form Question Answering — Fan 2019" description: "Most question answering benchmarks target short or span-extracted answers, so explanatory \"how\" and \"why\" questions remain largely unaddressed." authors: ["Angela Fan", "Yacine Jernite", "Ethan Perez", "David Grangier", "Jason Weston", "Michael Auli"] year: 2019 venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics" source: "https://aclanthology.org/P19-1346/" doi: "10.18653/v1/p19-1346" arxiv: "1907.09190" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2019](https://md.research.elamur.ai/free/en/papers/2019/page/2/) / ELI5: Long Form Question Answering # ELI5: Long Form Question Answering **Authors:** [Angela Fan](https://md.research.elamur.ai/free/en/authors/angela-fan/), [Yacine Jernite](https://md.research.elamur.ai/free/en/authors/yacine-jernite/), [Ethan Perez](https://md.research.elamur.ai/free/en/authors/ethan-perez/), [David Grangier](https://md.research.elamur.ai/free/en/authors/david-grangier/), [Jason Weston](https://md.research.elamur.ai/free/en/authors/jason-weston/), [Michael Auli](https://md.research.elamur.ai/free/en/authors/michael-auli/) · **Year:** 2019 · **Venue:** Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/P19-1346/) · [PDF](https://aclanthology.org/P19-1346.pdf) · [DOI 10.18653/v1/p19-1346](https://doi.org/10.18653/v1/p19-1346) · [arXiv 1907.09190](https://arxiv.org/abs/1907.09190) · [OpenAlex](https://openalex.org/W2950681488) **Citations:** 317 citations (openalex-snapshot · 2026-10-14) · [365](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary Most question answering benchmarks target short or span-extracted answers, so explanatory "how" and "why" questions remain largely unaddressed. This work defines long-form question answering and builds ELI5, a large corpus from the Reddit forum "Explain Like I'm Five." Each question is paired with the top-voted community answer, filtered by vote scores, and with evidence from Common Crawl. For every question, 100 web pages are retrieved with Apache Lucene and condensed through TFIDF-based passage selection into a single support document. The authors compare extractive approaches (including a BidAF-based model adapted to multi-sentence output), language models, and Seq2Seq models. The strongest system is a Seq2Seq model trained jointly on language modeling, masked word prediction, and answer generation, which outperforms the other baselines in both ROUGE and human judgments. Human-written answers still clearly win, and analysis shows that the compressed evidence omits much of the useful information. ELI5 therefore provides a testbed for retrieval over long inputs and for generating coherent paragraph-length answers. ## Key points - ELI5 contains about 272K question–answer pairs, split into 237K train, 10K validation, and 25K test. The validation and test questions were chosen so that none has a close TFIDF neighbor in training. - Average lengths are roughly 42 words per question, 858 words per support document, and 131 words per answer, about an order of magnitude longer than answers in SQuAD, TriviaQA, or MS MARCO. - On a manually annotated subset, 65% of support documents contained the answer and 92% held relevant information. In 63% of threads, at least one additional answer meets the vote threshold. - In pairwise human evaluation, raters chose the gold answer over every model in at least 85.5% of cases. The multi-task Seq2Seq model was preferred to the extractive baseline in only 57% of comparisons. - Oracle experiments show that fewer than 9% of the best-matching sentences come from the top 20 retrieved passages. Training on more data steadily raises ROUGE. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) ## Cited by Cited by 365 papers of our library cite it (of the 140884 whose bibliographies we read); 43 of them have a page here, 322 not yet. - [AmQA: Amharic Question Answering Dataset](https://md.research.elamur.ai/free/en/papers/abedissa-2023-amqa-amharic-question-answering-dataset/) - [Task-aware Retrieval with Instructions](https://md.research.elamur.ai/free/en/papers/asai-2023-task-aware-retrieval-instructions/) - [LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/) - [Olmo 3](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/) - [Reducing Transformer Depth on Demand with Structured Dropout](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/) - [Enabling Large Language Models to Generate Text with Citations](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/) - [RARR: Researching and Revising What Language Models Say, Using Language Models](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/) - [Retrieval-Augmented Generation for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/) - [Improving alignment of dialogue agents via targeted human judgements](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/) - [How Close is ChatGPT to Human Experts? 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BibTeX from the library's record of the work. ```bibtex @inproceedings{fan2019eli5, title = {{ELI5: Long Form Question Answering}}, author = {Fan, Angela and Jernite, Yacine and Perez, Ethan and Grangier, David and Weston, Jason and Auli, Michael}, booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year = {2019}, doi = {10.18653/v1/p19-1346}, eprint = {1907.09190}, archivePrefix = {arXiv}, url = {https://doi.org/10.18653/v1/p19-1346} } ``` - [← previous EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/) - [next → Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings](https://md.research.elamur.ai/free/en/papers/nguyen-2019-employing-correspondence-relations-connectives-identify/) - [↑ back to papers of 2019](https://md.research.elamur.ai/free/en/papers/2019/page/2/) ## References 27 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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